Identification of Arterial Disease Patients for Follow-Up

This application claims the benefit of priority from U.S. Provisional Patent Application No. 63/500,376, filed May 5, 2023, the entire contents of which are incorporated herein by reference.

This disclosure relates to identifying arterial disease patients for additional follow-up treatment or monitoring.

Patients having vascular issue(s), such as coronary arterial disease, may have varying disease severity and varying issues. Surgery on such patients to treat the vascular issue(s) may cause other vascular issue(s) to appear. For example, removal or bypass of a lesion in one portion of the vasculature of a patient may cause or reveal additional issue(s) within the vasculature of that patient.

Medical conditions, such as coronary arterial disease, may develop over years or even decades. As coronary arterial disease develops and modifies blood flow, the arterial bed may remodel to be optimal for the current blood flow conditions. For example, the arterial tree may remodel to maintain wall shear stress within an optimal range.

At some time, the arterial disease may reach a point that requires medical intervention. At the time of medical intervention, the disease is treated and years of atherosclerotic impact are reversed immediately, or almost immediately. However, the new flow conditions are not consistent with the years of remodeling that have occurred. This remodeling process may be a relatively slow process that may not be rapid enough to quickly catch up with the change in flow conditions caused by a medical intervention. As such, a subset of patients being treated for coronary arterial disease may develop new disease associated with a mis-match between the new blood flow and the remodeled arterial tree.

It is becoming more common to develop a computational model of the heart, for example, pre-procedurally using computed tomography (CT) data, or peri-procedural data using fluoroscopic data. Such techniques have been successful in identifying culprit lesions by calculating fractional flow reserve (FFR) values or other metrics of the disruption to the ideal or maximal flow caused by the presence of a culprit lesion.

Because patients having vascular issue(s), such as coronary arterial disease, may have varying disease severity and varying issues, patients may have different levels of need for treatment. For example, one patient with coronary arterial disease may be treated to remove or bypass a lesion. Such treatment on the patient with coronary arterial disease may reveal other issues in the patient due to the mismatch of the new blood flow and the remodeled arterial tree. While a second patient treated in the same general manner, may not experience the other issues which the first patient experienced. As such, it may be desirable to identify and distinguish which patients may require further, more urgent treatment than those patients who do not, so as to more efficiently utilize medical resources on the patients requiring more urgent treatment. Additionally, in some examples, it may be desirable to make such an identification during a diagnostic procedure, prior to any medical intervention. By doing so, one may improve patient outcomes, particularly for those patients who may require more medical attention than other patients.

In one example, the disclosure describes a medical system comprising memory configured to store a first computational model of a first patient vasculature; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine a first culprit lesion based on the first computational model of the vasculature of the first patient; virtually remove the first culprit lesion from the first computational model to generate a revised first computational model; determine a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determine that at least one of the first shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identify the patient as a member of a first patient group for follow-up treatment.

In another example, the disclosure describes a method comprising: determining, by processing circuitry, a first culprit lesion based on a first computational model of vasculature of a first patient; virtually removing, by the processing circuitry, the first culprit lesion from the first computational model to generate a revised first computational model; determining, by the processing circuitry, a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determining, by the processing circuitry, that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identifying, by the processing circuitry, the patient as a member of a first patient group for follow-up treatment.

In yet another example, the disclosure describes a non-transitory computer readable medium comprising instructions, which, when executed, cause processing circuitry to determine a first culprit lesion based on a first computational model of vasculature of a first patient; virtually remove the first culprit lesion from the first computational model to generate a revised first computational model; determine a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determine that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identify the patient as a member of a first patient group for follow-up treatment.

These and other aspects of the present disclosure will be apparent from the detailed description below. In no event, however, should the above summaries be construed as limitations on the claimed subject matter, which subject matter is defined solely by the attached claims.

This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the apparatus and methods described in detail within the accompanying drawings and description below. Further details of one or more examples are set forth in the accompanying drawings and the description below.

Medical resources are typically limited. To most efficiently use the limited medical resources, patients requiring more medical attention may be prioritized over patients requiring less medical attention. As such, it may be desirable to identify those patients requiring more medical attention and prioritize them for further medical treatment over patients not requiring more, or requiring less, medical attention. Doing so may improve patient outcomes and conserve medical resources, such as clinician time, hospital beds, equipment used during procedures, or the like.

A medical system may determine a culprit lesion in vasculature of a patient, for example, based on FFR values. The medical system may virtually remove the culprit lesion and recompute the fluid dynamics calculation to determine new velocity contours throughout an arterial tree. The medical system may also determine the shear stress along the arterial walls (e.g., using the velocity gradient at the vessel wall). The medical system may determine a subset of patients where the new shear stress is no longer within an optimal range at one or more places in the arterial tree. Additionally, or alternatively, the system may determine a further subset of patients where the shear stress is not in the optimal range at specific locations, for example, at or near a carina, or a side-branch bifurcation etc.

The system may stratify the sub-groups described above (shear stress outside normal range) as high risk and identify those patients to receive a more intensive follow-up protocol over time, such as incremental stress tests, CT scans, or more invasive testing, such as angiography. Other sub-groups where the new shear stress is considered to be within an optimal range might be deprioritized from an extensive follow-up and treated with less intensive follow up. In some examples, the stratification may occur in real-time while a patient is undergoing a medical procedure. In some examples, the stratification may occur after the medical procedure is complete, for example, hours, days, weeks, or the like, after the medical procedure is complete.

Aspects of this disclosure are applicable to at least Catheterization lab (Cath lab) procedures. Example Cath lab procedures include, but are not necessarily limited to, coronary diagnostic procedures, coronary intervention procedures (e.g., treatments for ST elevation myocardial infarction (STEMI), treatments for chronic total occlusion (CTO), coronary artery bypass graft (CABG), or the like), renal denervation (RDN) procedures, structural heart and aortic (SH&A) procedures (e.g., transcatheter aortic valve replacement (TAVR), transcatheter mitral valve replacement (TMVR), and the like), device implantation procedures (e.g., heart monitors, pacemakers, defibrillators, and the like), etc.

1 FIG. 100 110 120 140 150 100 100 100 160 100 100 100 is a schematic perspective view of one example of a system for stratifying patients according to one or more aspects of this disclosure. Systemincludes a display device, a table, an imager, and a computing device. Systemmay be an example of a system for use in an emergency room or a Cath lab. In some examples, systemmay include other devices, not shown for simplicity purposes. In some examples, systemmay also include server, which may be co-located with the other devices of systemor may be located elsewhere. Systemmay be used during a medical procedure, such as an emergency medical procedure to treat a medical condition of a patient. During such a procedure, systemmay identify a patient as a member of a first patient group for follow-up treatment or a second patient group. The first patient group may have a higher priority for follow-up treatment than the second patient group.

100 Systemmay include one or more machine learning models. A machine learning model may be trained to create a computational model of anatomy of the patient. For example, a machine learning model may be trained using image data (CT data, fluoroscopy data, other imaging data, etc.), FFR data, and/or the like, to create the computational model. The machine learning model may also be trained, for example, using image data, FFR data, and/or the like, to generate a revised computational model of anatomy of the patient, which may represent the anatomy of the patient with certain anatomy changed, for example, a lesion removed, which may affect the flow stresses within the anatomy.

150 150 140 150 150 140 140 Computing devicemay include, for example, an off-the-shelf device such as a laptop computer, desktop computer, tablet computer, smart phone, or other similar device or may include a specific purpose device. Computing devicemay perform various control functions with respect to imager. In some examples, computing devicemay include a guidance workstation. Computing devicemay control the operation of imagerand receive the output of imager.

110 120 Display devicemay be configured to output instructions, images, and messages relating to the medical procedure. Tablemay be, for example, an operating table or other table suitable for use during a medical procedure.

1 FIG. 140 140 140 In the example of, imager, such as a fluoroscopy imager, an angiography imager, or other imaging device, may be used to image relevant portions of the patient's anatomy during the medical procedure to visualize the anatomy, characteristics and locations of lesions or other issues inside the patient's body through the generation of imaging data. While described primarily as a fluoroscopy imager, imagermay be any type of imaging device, such as an angiography device, an intravascular ultrasound IVUS device, an OCT—FFR device, a CT device, an MRI device, a PET device, an ultrasound device, or the like. In some examples, imagermay represent more than one imaging device, such as a plurality of any of the aforementioned devices.

140 Imagermay image a region of interest in the patient's body. The particular region of interest may be dependent on anatomy, the medical procedure, patient symptoms, and/or the like. For example, when performing a cardiac medical procedure, a portion of the vasculature and/or the heart may be within the region of interest.

150 140 110 160 160 160 150 160 140 100 140 150 160 Computing devicemay be communicatively coupled to imager, display deviceand/or server, for example, by wired, optical, or wireless communications. Servermay be a hospital server which may or may not be located in an emergency room or Cath lab of a hospital, a cloud-based server, or the like. Servermay be configured to store patient imaging data, electronic healthcare or medical records or the like. Computing deviceand/or servermay utilize image data from imagerand/or other data generated by devices of system, to generate a computational model of vasculature of a patient. For example, imagermay generate image data of the vasculature of the patient during a diagnostic medical procedure and/or during a therapeutic medical procedure. Computing deviceand/or servermay use such image data to generate a computational model, such as a 3D model, of the vasculature of the patient.

150 140 160 150 140 160 150 140 160 150 140 160 150 140 160 150 140 160 Any of, or any combination of, computing device, imager, and/or servermay include a first computational model of vasculature of a first patient. For example, computing device, imager, and/or servermay determine a first culprit lesion based on the first computational model of the vasculature of the first patient. Computing device, imager, and/or servermay virtually remove the first culprit lesion from the first computational model to generate a revised first computational model. For example, this revised first computational model may be similar to the first computational model, but without the first culprit lesion being represented in the first computational model. With the first culprit lesion removed, blood flow within the vascular may change causing different pressures than with the first culprit lesion present. As such, computing device, imager, and/or servermay determine a first shear stress along first arterial walls in a first arterial tree based on the revised first computational model. Computing device, imager, and/or servermay compare the first shear stress to at least one threshold. Computing device, imager, and/or servermay determine that the first shear stress meets the at least one threshold in at least one location within the first arterial tree. For example, the at least one threshold may be relatively high enough to indicate that shear stress at the particular location of the revised first computational model may be higher than desired. As used herein, meeting a threshold may include being greater than the threshold, being greater than or equal to the threshold, being less than the threshold, or being less than or equal to the threshold, depending on the threshold itself and/or the usage of the threshold.

150 140 160 150 140 160 Computing device, imager, and/or servermay, based on the first shear stress meeting the at least one threshold, identify the patient as a member of a first patient group for follow-up treatment. For example, computing device, imager, and/or servermay identify the patient as being a relatively high-risk patient who should be prioritized for follow-up, such as further treatment or more intensive monitoring, over patients for whom the shear stress may not meet the threshold. As such, further testing and/or treatment may allow a clinician to more wholistically address cardiac issues of the patient and/or lower the risk to the patient of actually removing the first lesion without addressing the likely shear stress to be caused by the removal of the first lesion.

150 140 160 150 140 160 150 In some examples, in addition to, or alternatively, computing device, imager, and/or servermay reassess the group for follow-up with which patient is associated. For example, the prediction of the post-treatment state may be incorrect where a lesion was not removed or otherwise treated (e.g., bypassed) in a same manner as it was virtually removed prior to the actual, physical treatment. In some examples, computing device, imager, and/or servermay determine whether treatment for the first culprit lesion was applied as predicted (e.g., the same as or substantially the same as the virtual removal) and whether additional imaging data is desirable or a clinician may so indicate via a user interface. When additional imaging data is obtained or desired to be obtained, computing devicemay obtain the additional image data and determine, based on the additional image data, whether the patient should remain a member of the first patient group for follow-up.

150 140 150 For example, computing devicemay generate a post treatment computational model based on the additional image data from imagerafter one or more lesions are actually treated. However, this may require a collection of a new set of image data which may expose the patient to additional X-rays and contrast. While such an example, may expose the patient to additional X-rays and contrast, it may be beneficial if an actual post-treatment state does not sufficiently match the pre-treatment prediction of the treatment effect and therefore pre-treatment prediction of future state may be in doubt. Computing devicemay then undertake a similar analysis of shear stresses as described above to determine whether the patient should remain a member of the first patient group or be assigned to another patient group.

2 FIG. 1 FIG. 150 10 150 is a schematic view of one example of a computing deviceof systemof. Computing devicemay include a workstation, a desktop computer, a laptop computer, a smart phone, a tablet, a dedicated computing device, or any other computing device capable of performing the techniques of this disclosure.

150 140 150 140 150 202 204 206 208 210 212 2 FIG. Computing devicemay be configured to perform processing, control and other functions associated with imager. As shown in, computing devicemay represent multiple instances of computing devices, each of which may be associated with imager. Computing devicemay include, for example, a memory, processing circuitry, a display, a network interface, input device(s), and/or output device(s), each of which may represent any of multiple instances of such a device within the computing system, for ease of description.

204 150 204 150 140 160 204 150 140 160 150 150 140 160 2 FIG. While processing circuitryappears in computing devicein, in some examples, features attributed to processing circuitrymay be performed by processing circuitry of any of computing device, imager, or server, or combinations thereof. In some examples, one or more processors associated with processing circuitryin computing system may be distributed and shared across any combination of computing device, imager, and server. Computing devicemay be used to perform any of the techniques described in this disclosure, and may form all or part of devices or systems configured to perform such techniques, alone or in conjunction with other components, such as components of computing device, imager, server, or a system including any or all of such systems.

202 150 204 150 140 202 202 204 Memoryof computing deviceincludes any non-transitory computer-readable storage media for storing data or software that is executable by processing circuitryand that controls the operation of computing deviceand/or imager, as applicable. In one or more examples, memorymay include one or more solid-state storage devices such as flash memory chips. In one or more examples, memorymay include one or more mass storage devices connected to the processing circuitrythrough a mass storage controller (not shown) and a communications bus (not shown).

204 150 Although the description of computer-readable media herein refers to a solid-state storage, it should be appreciated by those skilled in the art that computer-readable storage media may be any available media that may be accessed by the processing circuitry. That is, computer readable storage media includes non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. For example, computer-readable storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, Blu-Ray or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store the desired information and that may be accessed by computing device. In one or more examples, computer-readable storage media may be stored in the cloud or remote storage and accessed using any suitable technique or techniques through at least one of a wired or wireless connection.

202 214 216 220 240 242 230 214 140 150 208 140 214 202 216 204 216 214 216 Memorymay store image data, computational model(s), revised computational model(s), patient groups, and threshold(s), and applications. Image datamay be generated by imagerof anatomy of the patient and obtained by computing devicevia network interfacewhich may be communicatively coupled to imager. In some examples, image datamay include image data of anatomy of interest of the patient, such as cardiac vasculature of the patient. Memorymay store computational model. For example, processing circuitrymay generate computational modelbased on image data. In some examples, computational modelmay include a 3D model of anatomy of the patient.

214 140 204 214 140 214 202 204 218 206 110 218 1 FIG. 1 FIG. Image datamay be captured by imager() during a medical procedure of a patient. Processing circuitrymay obtain imaging datafrom imagerand store image datain memory. Processing circuitrymay execute user interfaceso as to cause display(and/or display deviceof) to present user interfaceto one or more clinicians performing the medical procedure.

230 202 222 218 222 204 216 214 220 204 226 204 204 216 Applicationsof memorymay include one or more machine learning model(s)and user interface. Machine learning model(s)may be configured to, when executed by processing circuitry, generate computational model(s)based on image dataand/or generate revised computational model(s). In some examples, processing circuitrymay determine a culprit lesion (e.g., of culprit lesion(s)) within the anatomy of the patient. For example, processing circuitrymay identify a culprit lesion, by determining that an FFR value at a particular location of the vasculature of the patient meets (e.g., is less than or less than or equal to) an FFR threshold. This may be indicative of a culprit lesion existing at that particular location. As such, processing circuitrymay identify the culprit lesion within a computational model of computational model(s)as being at a location within the computational model that corresponds to the location within the vasculature of the patient having the FFR value that meets the threshold.

204 226 216 220 204 228 220 204 228 Processing circuitrymay virtually remove culprit lesionfrom computational modelto generate a revised computational model (for example, of revised computational model(s)). Processing circuitrymay determine a shear stress along arterial walls in an arterial tree (e.g., of arterial walls/arterial tree(s)) based on a revised computational model of revised computational model(s). For example, processing circuitrymay determine a velocity gradient at a vessel wall (e.g., an arterial wall of arterial walls/arterial tree).

204 242 204 204 204 Processing circuitrymay compare the shear stress to a threshold (for example, of threshold(s)). If the shear stress meets the threshold (e.g., is greater than or greater than or equal to), processing circuitrymay identify the patient as a member of a first patient group (e.g., of higher risk patients) for follow-up, such as further treatment or more intensive monitoring. If the shear stress does not meet the threshold, processing circuitrymay identify the patient as a member of a second patient group which may be a group of lower risk patients. Processing circuitrymay assign this second patient group of lower risk patients a lower priority for follow-up than the first patient group. For example, follow-up may include treatment and/or monitoring such as an incremental stress test, a CT scan, an angiography, or other technique to better investigate or treat the location(s) where the shear stress meets the threshold.

In some examples, there may be more than two patient groups. For example, there may be a plurality of first patient groups, each being associated with a shear stress meeting the threshold at a different particular location of the vasculature. For example, there may be a first patient group for patients whose shear stress meets the threshold at a carina. There may be another first patient group for patient whose shear stress meets the threshold within, for example, 5 mm of a carina. There may be another first patient group for patient whose shear stress meets the threshold at a side-branch bifurcation. There may be another first patient group for patient whose shear stress meets the threshold within, for example, 5 mm of a side-branch bifurcation. In this manner, the different first patient groups may be treated differently to schedule follow-up treatments more relevant for the areas of the vasculature for which the shear stress met the threshold. In some examples, there may be a plurality of thresholds, for example, one for each location in the vasculature.

204 204 150 In some examples, processing circuitrymay automatically schedule a patient for follow-up when the patient is identified as a member of the first patient group, but not automatically schedule another patient for follow-up treatment when that other patient is a member of the second patient group. In some examples, processing circuitrymay flag patient for follow-up treatment when the patient is identified as a member of the first patient group, but not flag another patient for follow-up treatment when that other patient is a member of the second patient group. The identity of such flagged patients may be sent to hospital staff responsible for scheduling follow-up appointments and the hospital staff may contact the flagged patients to schedule the follow-up appointments. In this manner, computing devicemay prioritize the scheduling of follow-up medical procedures for the first group of relatively higher risk patients.

204 216 214 204 214 216 216 In some examples, processing circuitrymay create computational modelbased on image data(e.g., fluoroscopy data). For example, processing circuitrymay use multiplane fluoroscopy image datato generate computational model. In some examples, computational modelmay include a 3D model of anatomy of the patient.

204 204 Processing circuitrymay be implemented by one or more processors, which may include any number of fixed-function circuits, programmable circuits, or a combination thereof. In various examples, control of any function by processing circuitrymay be implemented directly or in conjunction with any suitable electronic circuitry appropriate for the specified function. Fixed-function circuits refer to circuits that provide particular functionality and are preset on the operations that may be performed. Programmable circuits refer to circuits that may programmed to perform various tasks and provide flexible functionality in the operations that may be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, the one or more of the units may be distinct circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

204 Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs) or other equivalent integrated or discrete logic circuitry. Accordingly, the term processing circuitryas used herein may refer to one or more processors having any of the foregoing processor or processing structure or any other structure suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated hardware or software modules configured for encoding and decoding, or incorporated in a combined codec. Also, the techniques could be fully implemented in one or more circuits or logic elements.

206 206 210 Displaymay be touch sensitive or voice activated (e.g., via one or more sensors which may include one or more microphones), enabling displayto serve as both an input and output device. Alternatively, a keyboard (not shown), mouse (not shown), or other data input devices (e.g., input device(s)) may be employed.

208 150 214 140 150 230 208 150 206 Network interfacemay be adapted to connect to a network such as a local area network (LAN) that includes a wired network or a wireless network, a wide area network (WAN), a wireless mobile network, a Bluetooth network, or the internet. For example, computing devicemay obtain image datafrom imagerduring a medical procedure. Computing devicemay receive updates to its software, for example, applications, via network interface. Computing devicemay also display notifications on displaythat a software update is available.

210 150 Input device(s)may include any device that enables a user to interact with computing device, such as, for example, a mouse, keyboard, foot pedal, touch screen, augmented-reality input device receiving inputs such as hand gestures or body movements, or voice interface.

212 Output device(s)may include any connectivity port or bus, such as, for example, parallel ports, serial ports, universal serial busses (USB), or any other similar connectivity port known to those skilled in the art.

230 202 204 150 204 218 214 216 220 226 228 206 110 Applicationsmay be one or more software programs stored in memoryand executed by processing circuitryof computing device. Processing circuitrymay execute user interface, which may display image data, representations of computational model, revised computational model, culprit lesion, arterial walls/arterial treeon displayand/or display device.

3 FIG. 3 FIG. 1 FIG. 204 is a flow diagram of example techniques for identifying patients for follow-up according to one or more aspects of this disclosure. The techniques ofare described below with respect to processing circuitry, but such techniques may be performed by any of, or any combination of, processing circuitry of devices depicted inor capable of performing such techniques.

204 300 204 226 Processing circuitrymay determine a first culprit lesion in the first computational model of the vasculature of the first patient (). For example, processing circuitrymay determine an FFR value meets an FFR threshold. The FFR value at a particular location meeting the FFR threshold, may indicate a culprit lesion exists at that location. The location in the computational model associated with the location of the patient having the FFR value meeting the FFR threshold may be identified as including culprit lesion.

204 302 204 226 216 220 Processing circuitrymay virtually remove the first culprit lesion from the first computational model to generate a revised first computational model (). For example, processing circuitrymay remove culprit lesionfrom a computational model of computational model(s)to generate a revised computational model of computational model(s).

204 304 204 228 Processing circuitrymay determine a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model (). For example, processing circuitrymay determine a shear stress map including a plurality of shear stress values along arterial walls/arterial tree(s).

204 306 204 228 Processing circuitrymay determine that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree (). For example, processing circuitrymay compare the values of a shear stress map to a threshold associated with a location within arterial walls/arterial tree(s)to determine that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree.

204 308 204 240 Processing circuitrymay, based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identify the first patient as a member of a first patient group for follow-up (). For example, processing circuitrymay, based on at least one value of the shear stress map meeting the at least one threshold, identify the first patient as a member of a first patient group of patient groupsfor follow-up, such as treatment and/or monitoring.

204 226 216 204 220 204 228 204 242 204 240 204 In some examples, processing circuitryis further configured to determine a second culprit lesion (e.g., of culprit lesions) based on a second computational model (e.g., of computational model(s)) of a vasculature of a second patient. In such examples, processing circuitryis further configured to virtually remove the second culprit lesion from the second computational model to generate a revised second computational model (e.g., of revised computational models). In such examples, processing circuitryis configured to determine a second plurality of shear stresses along arterial walls in a second arterial tree (e.g., of arterial walls/arterial tree(s)) based on the revised second computational model. In such examples, processing circuitryis configured to determine that none of the second plurality of shear stresses meet the at least one threshold (e.g., of threshold(s)) in at least one location within the second arterial tree. In such examples, processing circuitryis configured to, based on none of the second plurality of shear stresses meeting the at least one threshold, identify the second patient as a member of a second patient group (e.g., of patient groups), wherein the second patient group comprises a lower risk patient group than the first patient group. In some examples, processing circuitryis further configured to prioritize, for follow-up, the first patient group over the second patient group.

204 In some examples, the follow-up includes at least one of an incremental stress test, CT scan, or angiography. In some examples, as part of determining the first culprit lesion, processing circuitryis configured to determine that an FFR value meets an FFR threshold.

In some examples, the at least one location comprises a predetermined location. In some examples, the predetermined location comprises at least one of at a carina, within 5 mm of a carina, at a side-branch bifurcation, or within 5 mm of a side-branch bifurcation.

204 In some examples, as part of determining the shear stress, processing circuitryis configured to determine a velocity gradient at a vessel wall. In some examples, the at least one threshold includes a plurality of thresholds each associated with a respective location within the arterial tree.

204 204 In some examples, processing circuitryis further configured to, based on the patient being identified as a member of the first patient group, at least one of flagging the patient for follow-up or automatically scheduling the patient for follow-up. In some examples, processing circuitryis further configured to obtain image data of the first patient vasculature and determine the first computational model of the first patient vasculature based on the image data.

204 214 140 204 In some examples, processing circuitrymay obtain additional image data of the first patient vasculature (e.g., of image data). The additional image data may be captured by an imaging device (e.g., imager) after a treatment for the first culprit lesion is performed, for example, after the first culprit lesion is removed, bypassed, or the like. Processing circuitrymay determine, based on the additional image data, whether the first patient should remain a member of the first patient group.

204 216 In some examples, as part of determining whether the first patient should remain a member of the first patient group, processing circuitrymay be configured to determine a post treatment computational model (e.g., of computational model(s)), the post treatment computational model being based at least in part on the additional image data.

204 204 204 204 In some examples, processing circuitryis further configured to determine a post treatment plurality of shear stresses along the first arterial walls in the first arterial tree based on the post treatment computational model. In such examples, processing circuitryis configured to determine that at least one of the post treatment plurality of shear stresses meets the at least one threshold in at least one location within the first arterial tree. In some examples, processing circuitryis configured to, based on the at least one of the post treatment plurality of shear stresses meeting the at least one threshold, determine that the first patient remain a member of the first patient group. In some examples, processing circuitryis configured to, based on the at least one of the post treatment plurality of shear stresses not meeting the at least one threshold, determine that the first patient be removed from the first patient group and be identified as member of the second patient group.

204 204 204 242 204 204 206 110 In some examples, processing circuitryis further configured to determine a difference between the treatment for the first culprit lesion and the virtual removal of the first culprit lesion. For example, processing circuitrymay determine that not all of the first culprit lesion which was removed virtually was actually physically removed or that the first culprit lesion was not actually removed at all, for example, that the first culprit lesion was bypassed. Processing circuitrymay determine that the difference meets at least one criterion (e.g., of threshold(s)). For example, the at least one criterion may include a threshold percentage of actual removal compared to the virtual removal, that the treatment was different than an attempted removal, or the like. Based on the difference meeting the at least one criterion, processing circuitrymay generate an indication suggesting that the additional imaging data be obtained. For example, processing circuitrymay output the suggestion to displayand/or display device, or send a communication to a clinician computing device (not shown) including the indication.

4 FIG. 400 222 400 216 220 150 160 400 100 400 is a conceptual diagram illustrating an example machine learning model according to one or more aspects of this disclosure. Machine learning modelmay be an example of machine learning model(s). Machine learning modelmay be an example of a deep learning model, or deep learning algorithm, trained to generate computational model(s)and/or revised computational model(s). One or more of computing deviceand/or servermay train, store, and/or utilize machine learning model, but other devices of systemmay apply inputs to machine learning modelin some examples. In some examples, other types of machine learning and deep learning models or algorithms may be utilized in other examples. For examples, a convolutional neural network model of ResNet-18 may be used. Some non-limiting examples of models that may be used for transfer learning include AlexNet, VGGNet, GoogleNet, ResNet50, or DenseNet, etc. Some non-limiting examples of machine learning techniques include Support Vector Machines, K-Nearest Neighbor algorithm, and Multi-layer Perceptron.

4 FIG. 400 402 404 406 406 405 406 402 400 214 400 100 As shown in the example of, machine learning modelmay include three types of layers. These three types of layers include input layer, hidden layers, and output layer. Output layercomprises the output from the transfer functionof output layer. Input layerrepresents each of the input values X1 through X4 provided to machine learning model. In some examples, the input values may include any of the values input into the machine learning model, as described above. For example, the input values may include image data, as described above. In addition, in some examples input values of machine learning modelmay include additional data, such as other data that may be collected by or stored in system.

402 404 404 402 404 400 214 216 220 400 400 4 FIG. Each of the input values for each node in the input layeris provided to each node of a first layer of hidden layers. In the example of, hidden layersinclude two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples. Each input from input layeris multiplied by a weight and then summed at each node of hidden layers. During training of machine learning model, the weights for each input are adjusted to establish the relationship between image dataand computational model(s)and/or revised computational model(s). In some examples, one hidden layer may be incorporated into machine learning model, or three or more hidden layers may be incorporated into machine learning model, where each layer includes the same or different number of nodes.

404 406 400 407 214 216 226 220 228 The result of each node within hidden layersis applied to the transfer function of output layer. The transfer function may be liner or non-linear, depending on the number of layers within machine learning model. Example non-linear transfer functions may be a sigmoid function or a rectifier function. The outputof the transfer function may be a classification that image datais indicative of aspects of a computational model of computational model(s), culprit lesion(s), revised computational model(s), and/or arterial walls/arterial tree(s).

400 214 204 As shown in the example above, by applying machine learning modelto input data such as image data, processing circuitryis able to determine a particular computational model and/or revised computational model. This may improve the placement of a patient into an appropriate patient group, which may improve patient outcomes while more efficiently using medical resources.

5 FIG. 570 222 400 574 400 222 150 160 574 572 572 572 204 222 216 220 is a conceptual diagram illustrating an example training process for a machine learning model according to one or more aspects of this disclosure. Processmay be used to train machine learning model(s)or machine learning model. A machine learning model(which may be an example of machine learning modeland/or machine learning model(s)) may be implemented using any number of models for supervised and/or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, naïve Bayes network, support vector machine, or k-nearest neighbor model, CNN, RNN, LSTM, ensemble network, to name only a few examples. In some examples, one or more of computing deviceand/or serverinitially trains machine learning modelbased on a corpus of training data. Training datamay include, for example, image data (CT data, fluoroscopy data, etc.) of anatomy of patients, FFR data, sensor data (e.g., from a wearable device, implantable device, stethoscope, etc.), and/or patient metadata (e.g., sex, age, weight, height, body mass index, body fat percentage, comorbidities, cholesterol level, blood pressure, blood oxygenation, physical exercise level, heart rate, or the like). Training datamay include data from past medical procedures performed on a plurality of patients having the same, similar, and/or different patient conditions, prior medical procedures, and/or the like. In some examples, processing circuitrymay execute machine learning model(s)to generate computational model(s)and/or revised computational model(s).

574 2 576 578 204 580 574 204 204 574 204 400 580 150 160 572 572 While training machine learning model, processing circuitry of systemmay comparea prediction or classification with a target output. Processing circuitrymay utilize an error signal from the comparison to train (learning/training) machine learning model. Processing circuitrymay generate machine learning model weights or other modifications which processing circuitrymay use to modify machine learning model. For examples, processing circuitrymay modify the weights of machine learning modelbased on the learning/training. For example, one or more of computing deviceand/or server, may, for each training instance in training data, modify, based on training data, the manner in which a computational model and/or revised computational is determined.

The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware or any combination thereof. For example, various aspects of the described techniques may be implemented within one or more processors or processing circuitry, including one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components. The terms “controller”, “processor”, or “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry. A control unit comprising hardware may also perform one or more of the techniques of this disclosure. Such hardware, software, and firmware may be implemented within the same device or within separate devices to support the various operations and functions described in this disclosure. In addition, any of the described units, circuits or components may be implemented together or separately as discrete but interoperable logic devices. Depiction of different features as circuits or units is intended to highlight different functional aspects and does not necessarily imply that such circuits or units must be realized by separate hardware or software components. Rather, functionality associated with one or more circuits or units may be performed by separate hardware or software components or integrated within common or separate hardware or software components.

The techniques described in this disclosure may also be embodied or encoded in a computer-readable medium, such as a computer-readable storage medium, containing instructions. Instructions embedded or encoded in a computer-readable storage medium may cause a programmable processor, or other processor, to perform the method, e.g., when the instructions are executed. Computer readable storage media may include random access memory (RAM), read only memory (ROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), or electronically erasable programmable read only memory (EEPROM), or other computer readable media.

Example 1. A medical system comprising: memory configured to store a first computational model of a first patient vasculature; and processing circuitry communicatively coupled to the memory, the processing circuitry being configured to: determine a first culprit lesion based on the first computational model of the vasculature of the first patient; virtually remove the first culprit lesion from the first computational model to generate a revised first computational model; determine a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determine that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identify the first patient as a member of a first patient group for follow-up. Example 2. The medical system of example 1, wherein the processing circuitry is further configured to: determine a second culprit lesion based on a second computational model of a vasculature of a second patient; virtually remove the second culprit lesion from the second computational model to generate a revised second computational model; determine a second plurality of shear stresses along arterial walls in a second arterial tree based on the revised second computational model; determine that none of the second plurality of shear stresses meet the at least one threshold in at least one location within the second arterial tree; and based on none of the second plurality of shear stresses meeting the at least one threshold, identify the second patient as a member of a second patient group, wherein the second patient group comprises a lower risk patient group than the first patient group. Example 3. The medical system of example 2, wherein the processing circuitry is further configured to prioritize, for follow-up, the first patient group over the second patient group. Example 4. The medical system of any of examples 1-3, wherein the follow-up comprises at least one of an incremental stress test, CT scan, or angiography. Example 5. The medical system of any of examples 1-4, wherein as part of determining the first culprit lesion, the processing circuitry is configured to determine a fractional flow reserve (FFR) value meets an FFR threshold. Example 6. The medical system of any of examples 1-5, wherein the at least one location comprises a predetermined location. Example 7. The medical system of example 6, wherein the predetermined location comprises at least one of at a carina, within 5 mm of a carina, at a side-branch bifurcation, or within 5 mm of a side-branch bifurcation. Example 8. The medical system of any of examples 1-7, wherein as part of determining the first plurality of shear stresses, the processing circuitry is configured to determine a velocity gradient at a vessel wall. Example 9. The medical system of any of examples 1-8, wherein the at least one threshold comprises a plurality of thresholds each associated with a respective location within the arterial tree. Example 10. The medical system of any of examples 1-9, wherein the processing circuitry is further configured to, based on the first patient being identified as a member of the first patient group, at least one of flagging the first patient for follow-up or automatically scheduling the first patient for follow-up. Example 11. The medical system of any of examples 1-10, wherein the processing circuitry is further configured to: obtain image data of the first patient vasculature; and determine the first computational model of the first patient vasculature based on the image data. Example 12. The medical system of any of examples 1-11, wherein the processing circuitry is further configured to: obtain additional image data of the first patient vasculature, the additional image data being captured by an imaging device after a treatment for the first culprit lesion is performed; and determine, based on the additional image data, whether the first patient should remain a member of the first patient group. Example 13. The medical system of example 12, wherein as part of determining whether the first patient should remain a member of the first patient group, the processing circuitry is configured to determine a post treatment computational model, the post treatment computational model being based at least in part on the additional image data. Example 14. The medical system of example 13, wherein the processing circuitry is further configured to: determine a post treatment plurality of shear stresses along the first arterial walls in the first arterial tree based on the post treatment computational model; determine that at least one of the post treatment plurality of shear stresses meets the at least one threshold in at least one location within the first arterial tree; and based on the at least one of the post treatment plurality of shear stresses meeting the at least one threshold, determine that the first patient remain a member of the first patient group. Example 15. The medical system of example 13, wherein the processing circuitry is further configured to: determine a post treatment plurality of shear stresses along the first arterial walls in the first arterial tree based on the post treatment computational model; determine that at least one of the post treatment plurality of shear stresses meets the at least one threshold in at least one location within the first arterial tree; and based on the at least one of the post treatment plurality of shear stresses not meeting the at least one threshold, determine that the first patient be removed from the first patient group and be identified as member of the second patient group. Example 16. The medical system of any of examples 12-15, wherein the processing circuitry is further configured to: determine a difference between the treatment for the first culprit lesion and the virtual removal of the first culprit lesion; determine that the difference meets at least one criterion; and based on the difference meeting the at least one criterion, generate an indication suggesting that the additional imaging data be obtained. Example 17. A method comprising: determining, by processing circuitry, a first culprit lesion based on a first computational model of vasculature of a first patient; virtually removing, by the processing circuitry, the first culprit lesion from the first computational model to generate a revised first computational model; determining, by the processing circuitry, a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determining, by the processing circuitry, that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identifying, by the processing circuitry, the first patient as a member of a first patient group for follow-up. Example 18. The method of example 17, wherein the processing circuitry is further configured to: determining, by processing circuitry, a second culprit lesion based on a second computational model of a vasculature of a second patient; virtually removing, by processing circuitry, the second culprit lesion from the second computational model to generate a revised second computational model; determining, by processing circuitry, a second plurality of shear stresses along arterial walls in a second arterial tree based on the revised second computational model; determining, by processing circuitry, that none of the second plurality of shear stresses meet the at least one threshold in at least one location within the second arterial tree; and based on none of the second plurality of shear stresses meeting the at least one threshold, identifying, by processing circuitry, the second patient as a member of a second patient group, wherein the second patient group comprises a lower risk patient group than the first patient group. Example 19. The method of example 18, further comprising prioritizing, by the processing circuitry and for follow-up, the first patient group over the second patient group. Example 20. The method of any of examples 17-19, wherein the follow-up comprises at least one of an incremental stress test, CT scan, or angiography. Example 21. The method of any of examples 17-20, wherein determining the first culprit lesion comprises determining that an FFR value falls below an FFR threshold. Example 22. The method of any of examples 17-21, wherein the at least one location comprises a predetermined location. Example 23. The method of example 22, wherein the predetermined location comprises at least one of at a carina, within 5 mm of a carina, at a side-branch bifurcation, or within 5 mm of a side-branch bifurcation. Example 24. The method of any of examples 17-23, wherein determining the first plurality of shear stresses comprises determining a velocity gradient at a vessel wall. Example 25. The method of any of examples 17-24, wherein the at least one threshold comprises a plurality of thresholds each associated with a respective location within the arterial tree. Example 26. The method of any of examples 17-25, further comprising, based on the first patient being identified as a member of the first patient group, at least one of flagging the first patient for follow-up or automatically scheduling the first patient for follow-up. Example 27. The method of any of examples 17-26, further comprising: obtaining, by the processing circuitry, image data of the first patient vasculature; and determining, by the processing circuitry, the first computational model of the first patient vasculature based on the image data. Example 28. A non-transitory computer-readable storage medium storing instructions, which when executed cause processing circuitry to: determine a first culprit lesion based on a first computational model of vasculature of a first patient; virtually remove the first culprit lesion from the first computational model to generate a revised first computational model; determine a first plurality of shear stresses along first arterial walls in a first arterial tree based on the revised first computational model; determine that at least one of the first plurality of shear stresses meets at least one threshold in at least one location within the first arterial tree; and based on the at least one of the first plurality of shear stresses meeting the at least one threshold, identify the first patient as a member of a first patient group for follow-up. This disclosure includes the following non-limiting examples.

Various examples have been described. These and other examples are within the scope of the following claims.